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dlt

dlt

Official
by dlt-hub
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Python
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get_load_table

Retrieve metadata about data loaded with a dlt pipeline. Provide the pipeline name to access load table details.

Instructions

Retrieve metadata about data loaded with dlt.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "pipeline_name": "<pipeline_name>"
    },
    "name": "get_load_table"
  }
}

Input Schema

NameRequiredDescriptionDefault
pipeline_nameYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.0

TDQS

C2.4/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It does not disclose any behavioral traits such as read-only nature, scope of metadata, or side effects. The description is too sparse to convey behavioral characteristics.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single short sentence with no wasteful words. It is front-loaded with the primary action. However, the extreme brevity comes at the cost of clarity and detail, which is a trade-off.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the presence of an output schema, the description does not need to explain return values, but it still lacks essential context. It does not define 'metadata,' differentiate from sibling tools, or explain the scope of the data load. The description is incomplete for an agent to use this tool correctly without additional knowledge.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description adds no meaning beyond the parameter name 'pipeline_name.' It does not explain what constitutes a valid pipeline name, how to find available pipelines, or any constraints. The description fails to compensate for the missing schema documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states 'Retrieve metadata about data loaded with dlt,' which is a specific verb+resource, but 'metadata about data loaded' is vague and does not clearly differentiate from sibling tools like get_table_schema or list_tables. The purpose is implied but not sharp.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when or when not to use this tool, nor any mention of alternatives among the sibling tools. The description leaves the agent to infer usage context from the name alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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